(Re)Defining Urban Education: A Conceptual Review and Empirical Exploration of the Definition of Urban Education

Abstract

Generally, educational studies do not problematize the definition of urban education or examine the positionality of sites along a spectrum of urban districts and schools. This study addresses the definitional gap by (a) examining the conceptualization of urban education through an integrative review of prior definitional research and (b) exploring how an urban district may be defined in empirical terms. Our findings indicate six categories are typically used to define urban education: (a) population/location/geography, (b) enrollment, (c) demographic composition of students, (d) resources in schools, (e) disparities and educational inequality, and (f) social and economic context. The results indicate that deficit-oriented language permeates prevailing definitions of urban education and that large-city-centered conceptualizations of urban education may overlook a substantial number of smaller districts with similar levels of educational inequality and diversity.

Keywords

descriptive analysis education equity education policy mixed methods urban districts urban education urban schools

There is an “identity crisis in urban education” that holds significant implications for the growth of a coherent and nuanced body of knowledge on urban education in the broader educational literature (Milner & Lomotey, 2014). Among educational researchers, practitioners, and policymakers, there is no universal definition of “urban education” (Buendía, 2011; Milner, 2012a; Milner et al., 2015; Noguera, 2003; Schaffer et al., 2018). Shifting demographics of neighborhoods, increasing diversity of students, as well as widespread disparities across and within districts and schools nationwide have further muddled the categorization of urban versus nonurban districts (Jacobs, 2015; Noguera, 2017). Few studies focus explicitly on how urban education is defined, and these studies have largely been limited to descriptive categories (Buendía, 2011; Irby, 2015; Milner, 2012a; Noguera, 1996; Schaffer et al., 2018).

This definitional gap has important educational policy, practice, and equity implications, beyond simple semantic clarification (Milner & Lomotey, 2014). Definitions are foundational to apprehending the magnitude of the phenomenon and crafting commensurate solutions in public education. Defining and operationalizing urban education establishes policy “boundaries” (Stone, 1997). The conceptualization of urban education shapes the perceptions, understanding, expectations, and practices of educators as well as teacher education (Jacobs, 2015; Milner et al., 2015; Schaffer et al., 2018). Different subsets of districts along the spectrum of “urbanness” may face distinct problems and require appropriate and commensurate solutions, making the nuances of defining urban education also important to school improvement conversations.

This study addresses the definitional gap by (a) examining the conceptualization of urban education through an integrative review of prior definitional research and (b) exploring how an urban district may be defined in empirical terms. We extend Milner’s (2012a) categorization of urban education into more granular and empirical groups in hopes of developing a typology that captures the growing variety of districts. This study is one of the first to link descriptive categorizations of urban education to quantitative classification of the degree of “urbanness” of districts in the United States. To be clear, this article does not claim to provide a definitive conceptualization but, rather, takes stock of existing conceptualization and offers research-based and empirically based perspectives that can advance the discourse on the definition of urban education.

Data and Methods

We conducted a comprehensive literature review to identify prior studies, articles, and reports that addressed the definition of urban education. A number of databases were used to systematically search for peer-reviewed articles, book chapters, theses and dissertations, and reports. Following the initial search of databases and abstract review, roughly 75 articles were selected. The full articles were then retrieved, read, and screened to ensure that all studies met the criteria for inclusion. Articles were excluded if (a) the study focused on urban education outside of the United States or (b) one of the aforementioned areas of interests (conceptualization, definition, or features/characteristics) were not the main focus of the study. Ultimately, 28 studies were included in the literature synthesis (the online appendix, which is available on the journal website, provides further details of the literature search). We use the findings of the literature review to inform the empirical exploration.

In order to explore empirical definitions of urban education, we use a district-level longitudinal dataset from the Stanford Education Data Archive (SEDA) Version 2.1 (Reardon, Ho, et al., 2018). The SEDA is an open data resource that brings togethers quantitative data from multiple sources including the National Center for Education Statistics and the American Community Survey (covariates in this analysis are derived primarily from the School District Demographics System [SDDS] and the Common Core of Data [CCD]). The data captures the demographic and socioeconomic characteristics as well as the academic achievement characteristics of nearly 22 million public school students annually in all school districts nationwide. In SEDA, charter schools are not considered separate school districts but are assigned to the traditional school districts in which they are located (the SEDA website provides further details on data construction). In the past decade, several papers have used the SEDA data to examine educational opportunity and student achievement among school districts (Bellows, 2018; Reardon, 2019; Reardon, Kalogrides, et al., 2018). The data span 7 years from 2008–2009 to 2014–2015 and have 93,534 district-years with more than 13,300 unique districts. The SEDA data include information on enrollment, achievement by student subgroups (third- through eighth-grade Math and English/Language Arts [ELA]), racial/ethnic and gender achievement gaps, and demographic and socioeconomic characteristics.

We examine statistical differences in characteristics that align with our literature review based on particular definitions of “urban school districts,” first through conditional descriptive statistics (see Table 1, below) for prominent definitions. The first definition is from Great Schools (www.greatschools.org), which considers all districts with student enrollments over 35,000 as urban districts (just over 100 districts in 2015). We then use the National Center for Education Statistics (NCES) definition of urban locale (roughly 800 districts) and a U.S. census-based indicator for “in Metropolitan Area”—being located geographically within a metropolitan statistical area (MSA) (just under 7,000 districts).

Table 1

Summary Statistics by Definition of “Urban School Districts” (2014)

	>35K Students		NCES Urban		In MSA		Urban Intensive		Urban Emergent
Characteristics	Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD
Size/complexity
Total enrollment	78,037.84	89,853.93	13,734.04	37,323.18	4,238.78	15,276.64	301,125.00	201,545.28	137,251.91	147,241.41
# schools	164.83	203.43	32.77	84.97	10.42	33.50	638.89	498.03	274.26	350.99
# teachers	5,891.36	6,897.35	988.80	2,722.05	340.54	1,166.11	21,358.24	16,518.31	10,493.44	11,939.32
# charters	25.62	40.21	4.37	16.18	0.84	6.20	112.89	80.30	41.78	47.88
% charters	0.09	0.09	0.06	0.12	0.02	0.08	0.15	0.09	0.10	0.11
Geographic code
City/urban locale	0.52	0.50	1.00	0.00	0.12	0.32	0.67	0.50	0.43	0.51
Suburban locale	0.48	0.50	NA	NA	0.42	0.49	0.33	0.50	0.57	0.51
Town locale	0.00	0.00	NA	NA	0.12	0.32	0.00	0.00	0.00	0.00
Student demographics
% White	0.34	0.22	0.45	0.27	0.69	0.29	0.18	0.11	0.35	0.21
% Black	0.25	0.21	0.19	0.22	0.09	0.17	0.29	0.15	0.25	0.18
% Latinx	0.33	0.23	0.29	0.26	0.17	0.23	0.47	0.18	0.32	0.18
% Asian	0.07	0.08	0.06	0.09	0.03	0.06	0.06	0.04	0.08	0.06
% Nat Am	0.01	0.01	0.01	0.04	0.02	0.07	0.00	0.00	0.00	0.00
% ELL	0.13	0.09	0.11	0.13	0.06	0.10	0.15	0.09	0.12	0.09
% spec ed	0.12	0.03	0.16	0.17	0.14	0.09	0.13	0.03	0.12	0.03
Educational inequality/segregation (isolation and exposure indexes)
Indx Wht/Blk	0.27	0.17	0.11	0.13	0.05	0.08	0.48	0.17	0.28	0.18
Indx Wht/Hsp	0.21	0.12	0.10	0.11	0.03	0.07	0.33	0.15	0.24	0.14
Indx FRL/Non-FRL	0.17	0.08	0.08	0.08	0.02	0.05	0.22	0.04	0.18	0.06
Exp Diff FRL/Non-FRL	0.22	0.10	0.10	0.10	0.03	0.06	0.29	0.07	0.23	0.09
Resources/expenditures
PPE total	11,755.76	3,948.76	12,573.48	4,446.60	13,728.74	5,149.89	13,464.96	5,687.89	12,729.34	5,312.72
PPE instructional	5,960.52	1,847.96	6,400.52	2,152.09	7,118.24	2,792.80	7,085.30	3,577.52	6,290.12	2,706.58
Student/teacher	17.75	3.45	19.32	44.03	17.42	49.38	18.23	2.99	16.91	2.61
Social/economic context
% poverty	0.15	0.07	0.17	0.08	0.11	0.08	0.18	0.05	0.13	0.06
% poverty 5–17	0.18	0.10	0.20	0.11	0.13	0.10	0.23	0.08	0.16	0.10
% single mom	0.31	0.12	0.31	0.12	0.22	0.11	0.37	0.08	0.29	0.13
% renters	0.39	0.14	0.40	0.14	0.26	0.15	0.50	0.12	0.36	0.15
Median income	57,977.64	22,664.63	55,242.87	22,842.51	70,517.47	30,664.13	44,875.22	9,381.02	65,400.43	28,033.81
Gini	0.41	0.05	0.40	0.05	0.35	0.06	0.44	0.03	0.40	0.06
90/10 income	10.09	3.63	10.19	3.57	7.77	3.84	11.20	2.65	9.98	3.86
% SNAP	0.18	0.11	0.20	0.12	0.13	0.11	0.21	0.09	0.14	0.10
% BA +	0.33	0.12	0.30	0.15	0.28	0.16	0.30	0.05	0.40	0.12
Total districts	110		814		7,073		9		23

Note. All descriptive statistics represent means and standard deviations for all U.S. school districts that meet the given criterion and are present in the Stanford Education Data Archive (SEDA) data set. NCES = National Center for Education Statistics; MSA = metropolitan statistical area; ELL = English language learners; FRL = Free and Reduced Price Lunch; PPE = per-pupil expenditure; SNAP = Supplemental Nutrition Assistance Program; BA = bachelor’s degree.

We also operationalize prior descriptive categories from Milner (2012a), namely, the “urban emergent” and the “urban intensive” classifications. We classify urban intensive districts as those nine districts with enrollments over 150,000: Los Angeles Unified School District (LAUSD), New York City (NYC) Public Schools, Houston Independent School District (ISD), Philadelphia City, Chicago, Dade (Miami), Broward (Ft. Lauderdale), Hillsborough (Tampa), and Clark County (Las Vegas, NV). We characterize “urban emergent” districts as those whose enrollment added more than 10,000 students in the 6 years captured in the SEDA data. These 23 districts experiencing considerable expansion include several of the already “urban intensive” districts but add diverse growing districts in cities like Washington, D.C., Nashville, Charlotte, Raleigh, Dallas, San Antonio, Salt Lake City, Orlando, and Metro Atlanta. Milner’s (2012a) third category, “urban characteristic,” explicitly focuses on “schools that are not located in big or midsized cities but may be starting to experience some of the challenges that are sometimes associated with urban school contexts in larger areas that were described in the urban intensive and urban emergent categories” (p. 559). Given that this group of districts may span rural and suburban contexts, and for brevity’s sake, we do not present results for this definitional category. However, we highlight examples of small districts in the data that have experienced substantial growth in the enumerated “urban characteristics.”

We then run a series of linear predictive models (see Figures 1 and 2, below) to assess whether different definitions result in substantively different sets of schools, or place greater emphasis on specific characteristics (e.g., racial composition) all else equal in a linear probability model (LPM) framework. In essence, we are predicting whether a district is “urban” based on a range of district-level characteristics. The primary model that we run separately for each of the definitions of “urban” school districts (predicting urban designation within the full sample of school districts in the country) takes the following form:

\begin{array}{l} U r b a n_{d} = β_{0} + β_{1} D e m o g r a p h i c s_{d} + β_{2} E d I n e q u a l i t y_{d} \\ + β_{3} R e s o u r c e s_{d} + β_{4} S E S C o m m u n i t y C o n t e x t_{c} + e_{d c} \end{array},

where $U r b a n_{d}$ is one of binary indicators (>35,000 students, NCES Urban, within MSA, urban intensive, and urban emergent) for urban classifications based on categories generated from our literature review. $D e m o g r a p h i c s_{d}$ is a vector of measures of districts demographic composition including the percentages of major racial and ethnic student groups. $E d I n e q u a l i t y_{d}$ is a vector of measures of racial, ethnic, and socioeconomic segregation, in the form of Black-White, Latinx-White, and Free and Reduced Price Lunch (FRL)–Non-FRL student Information Indexes. $R e s o u r c e s_{d}$ includes measures of pupil-teacher ratios as well as per-pupil total and instructional expenditures and student-teacher ratios. Finally, the largest set of variables fall into the category of social and economic community context $(S E S C o m m u n i t y C o n t e x t_{c})$ , including poverty rates, unemployment, participation in the Supplemental Nutrition Assistance Program (SNAP), measures of income inequality (GINI coefficient), percentage of households headed by single mothers, and median income level.

For ease of interpretation and comparability, each district characteristic is standardized to have a mean of zero and a standard deviation of one, such that the average district in the country would have 0 for each category, and the coefficients represent increase in the likelihood of a district being categorized as urban, for a one SD shift in the characteristic, all else equal. We also run models with alternative variables that have been less salient in definitional literature but represent primary market-based educational reforms in urban education, such as the percentage of students who are enrolled in charter schools (see online appendix on the journal website for further details). Results in the manuscript are based on the most recent available data, such that school characteristics are generally from 2014 and county characteristics are from the 2010 Decennial Census, but are qualitatively similar when restricted to 2010 school data. Results are also robust to alternative prediction models including logistic and probit regression, available upon request.

Results

Conceptualizing Urban Education

Two main themes emerge from the integrative review of definitions of urban education: (a) urban education is defined in multifaceted ways, and (b) urban education embodies deficit perspectives. Definitions of urban education are articulated in largely descriptive rather than empirical terms. First, the conceptualization of urban education is multifaceted, and most of the studies employed two or more categories in their definition of urban education. This is unsurprising given that urban education is a socially constructed and contested concept, and there is wide variation in how people view and experience education in urban settings. Our review indicates that six categories are typically used to define urban education: (a) population/location/geography, (b) enrollment, (c) demographic composition of students, (d) resources in schools, (e) disparities and educational inequality, and (f) social and economic context.

The vast majority of studies ground their definitions of urban education in location, size, and population (Schaffer et al., 2018). Almost all studies considered student demographics and race/ethnicity in their definition of urban education. Schaffer et al. (2018) highlighted that it is difficult to neglect issues of race when the context of urban schools is considered. Although elements of city, neighborhoods, schools, and students are all seemingly embedded implicitly and explicitly in the definition of urban education, over time, conceptualizations of urban education have increasingly acknowledged the nuanced interaction between inside and outside of school characteristics. For example, Milner (2012a) used size and population density of environments (over 1 million), students, resources (transportation, business infrastructure), and outside of school factors (housing and poverty) in conceptualizing urban education. Milner’s (2012a) definition encompasses interconnectivity between size and the racial and social context of districts—the scope and concentration of people are related to the outside of school factors that influence learning in the classrooms.

Urban education is often described in geographical terms—urban connotes place (Haymes, 1995; Kelley, 1997; Noguera, 2003). “Urban” refers to a region that encompasses a city and surrounding areas, thus capturing towns, cities, and suburbs (National Geographic, 2017). Larger, metropolitan areas are viewed as more urban, and the density of population (and accompanying commercial developments and the use of land) generally differentiates urban from rural areas. Much of the literature grounds the definition of “urban education” in the size and population density of a locale (Matsko & Hammerness, 2014; Milner, 2006, 2012a; Milner & Lomotey, 2014; Ratcliff et al., 2016; Rury & Mirel, 1997; Schaffer et al., 2018; Steinberg & Kincheloe, 2004; Watson, 2011). On average, urban districts are situated in areas with high population density (Hopson et al., 2007; Kincheloe, 2010; Ratcliff et al., 2016; Steinberg & Kincheloe, 2004). Buendía (2011) posited that urban signifies not just the place but also denotes particular meanings of “urban” populations and highlighted that place and people have been conflated in the definition of urban education. Urban education collapses race (people), place, and space.

Urban districts are also generally defined by size. For instance, urban school districts may be defined as being located in cities with a population greater than 250,000 and student enrollments of more than 35,000 (Council of the Great City Schools, 2013). Urban districts are generally larger than nonurban districts, serve significantly more students, and are more likely to experience overcrowding (Horng, 2005; Kincheloe, 2010; Lippman et al., 1996; Matsko & Hammerness, 2014). The racial/ethnic, linguistic, socioeconomic, and cultural characteristics of students are also commonly used to define urban education. Racially, ethnically, and linguistically diverse students attend urban schools at higher rates than nonurban schools (Gallagher et al., 2013; Hampton et al., 2008; Hopson et al., 2007; Kincheloe, 2010; Matsko & Hammerness, 2014; Milner, 2006; Milner & Lomotey, 2014; Steinberg & Kincheloe, 2004; Watson, 2011; White et al., 2017). Urban school districts are the meeting places of cultures and communities—densely populated, epicenters of commerce that attract a diverse set of people of varying ethnic, racial, linguistic, and geographic origins.

Darling-Hammond (2014) posited that urban school districts can be defined by underresourced schools (Milner & Lomotey, 2014). Being underresourced or deficient in some manner was a key component of “real urban schools” according to teacher learners (Jacobs, 2015). Urban schools also struggle with attracting high-quality teachers and school leaders (Chou & Tozer, 2008; Hudley, 2013; Kincheloe, 2010; Milner, 2006). Urban schools tend to be located in worn down facilities with limited and/or outdated resources (e.g., textbooks and technology) (Hampton et al., 2008; Horng, 2005; Hudley, 2013; Kincheloe, 2010; Matsko & Hammerness, 2014; Milner, 2006, 2012a; Milner & Lomotey, 2014; Watson, 2011; White et al., 2017). Darling-Hammond (2014) argued that urban school districts can be defined by the concentration of inequality and characterized by segregation (Milner & Lomotey, 2014). Urban schools are situated in areas with economic hardship and concentrated poverty (Breault & Allen, 2008; Hudley, 2013; Kincheloe, 2010; Lippman et al., 1996; Milner, 2012a; Steinberg & Kincheloe, 2004; Watson, 2011). The urban context is one defined by considerable education debt (Ladson-Billings, 2006).

Recent definitions of urban education have moved beyond isolated population models to address the degree to which out of school factors (social and economic context) may shape educational practices and outcomes (e.g., poverty levels within districts, racial and ethnic diversity, poor achievement scores, poorly maintained facilities, and teacher quality) within districts. These definitions emphasize sociological factors that influence learning in urban environments and compel researchers and policymakers to expand the understandings of the urban students, families, and communities from a superficial to a deeper meaningful understanding that can effectively shape educators’ roles and practices. There is a firm connection between urban education and poverty (Milner et al., 2015). Milner et al. (2015) argued that outside of school factors are important in supporting students in urban districts and schools and that these factors shape the understanding and practices of educators in meeting students’ needs. Several scholars have argued that teachers should be aware of the realities inside and outside of school (Milner, 2012b; Milner & Lomotey, 2014; Noguera, 2014).

Urban education embodies deficit perspectives

Deficit-oriented language (a perspective that “urban” is synonymous with being deficient) in the conceptualization of urban education is pervasive, and several scholars have highlighted the need to carefully consider the use and meaning of the term urban education (Buendía & Ares, 2006; Donnell, 2010; Jacobs, 2015; Milner, 2012a; Popkewitz, 1998). Deficit perspectives plague the vast majority of research on urban education and theory of urban education research (Donnell, 2010; Jacobs, 2015; Weiner, 2006; White et al., 2017). Jacobs (2015) found that deficit perspectives in the conceptualization of urban schools were common among teacher learners (preservice teachers). Scholarly and public representations of urban schools and students play a critical role in how urban schools and students are perceived (Gadsden & Dixon-Román, 2017). Common perceptions include, but are not limited to, that urban students are less likely to attend college or complete college, urban students are difficult to manage, and urban schools are low quality (Gadsden & Dixon-Román 2017; White et al., 2017). Shared understandings of urban education are usually rooted in conceptualizations largely based on the issues and challenges facing these districts and schools (Jacobs, 2015). There are safe, academically successful, and desirable urban schools. Urban is success as well as failure. Yet failures are often the main axis of a deficit orientation and success is regarded as “not really urban” (Jacobs, 2015).

In many ways, “urban education” is often used as a palatable way to signify underperformance in educational outcomes that glosses over complex social issues largely affecting people from marginalized and oppressed backgrounds. Generally, “urban” is tied to race and class and may perpetuate stereotypes (Milner, 2012a) and is typically used as a code word for Black, poor, and uneducated (Jacobs, 2015). “Urban” is also used to describe a lack of community and family support in addition to a codeword for race (Watson, 2011). A sociological oriented definition of urban education is, in some ways, antideficit, antigeneralization, and antiessentialization. This broader conceptualization of urban education focuses on the causes (the inside and outside of school factors shaping learning) rather than the outcomes associated with urban schools. Such a conceptualization shifts the conversation from a deficient perspective (blaming the challenges and context of urban schools on the characteristics of people living in urban environments) to a sociological perspective rooted in education, economic, health, and social policy and reform over time. The crisis in public schools is not simply due to reckless individual choices but rather systemic institutional failures.

Quantifying Urban Education

Table 1 shows the descriptive statistics of the five definitions of urban education described in the methods section and illustrates a few commonalities and many variations. The average enrollment in districts is roughly 2,700 students with a median of 800 students (there are a handful of very large districts). Indeed, 90% of districts in the United States have fewer than 6,000 students. There are many small and midsize districts and few very large districts. It is unsurprising, for example, that the “urban intensive” designation captures a set of districts with radically larger enrollments (mean enrollment over 300,000) than any other. The “urban emergent” districts are the second largest, with average enrollments over 135,000; followed by the Great Schools categorization, which average 78,000; then NCES Urban at 13,000; followed by the much smaller, high-variance district size of the average district within an MSA (~4,000 mean students with a standard deviation of over 15,000 students). This large set of districts comes closest to the much smaller national average enrollment.

Although several conceptualizations of urban education are rooted primarily on enrollment size, the conditional means across a range of important other components indicate that many smaller districts (e.g., those classified by the NCES Urban model) share important “urban” characteristics with their larger counterparts. Another way that an emphasis on size can eschew urban characteristics is the magnitude of many large suburban districts. With the exception of the NCES Urban districts, all of our “urban district” definitions include districts that are primarily located in large suburbs (population greater than 250,000). Even the “urban emergent” and “urban intensive” categories, which are restricted to some of the largest districts in the country, include several massive suburban districts in sprawling metropolitan areas like Atlanta and Tampa.

Demographic composition is also used frequently in defining urban education. Our results indicate that White students make up a minority of district enrollments under every designation other than “within MSA.” However, this group (districts in MSAs) has the largest variance in the White population, with a standard deviation of nearly 30 percentage points, potentially reflecting its inclusion of both city and historically White suburban districts. The average district in each category has roughly one third students who are classified as Latinx, except in “within MSA,” where they drop to 17% on average. The proportion of Latinx students is highest in the “urban intensive” districts (47%). The proportion of Black students follows a similar but slightly less extreme pattern, representing roughly a quarter of students in most urban district classifications, but only 9% of districts “within MSA” and 29% in the “urban intensive” districts. Notably, Asian students are better represented in most urban classifications, and Native American students are almost entirely absent in the larger urban categories. English language learners consistently make up 11% to 15% of students regardless of the definition but are most concentrated in the “urban intensive” districts, which also notably have the highest shares of Black and Latinx students.

Most definitions of “urban districts” have higher average student teacher ratios than the national average of 15.8, but there is considerable variation in overall and instructional expenditures both within and across classifications. The highest expenditure districts on average per pupil are those “within MSA,” which is perhaps unsurprising as these districts include both high tax-base, high-cost central urban districts and all the small wealthy districts that form outside of city perimeters in economically connected suburbs and towns and have considerably higher median incomes than any other designation. Notably, even these highest per-pupil expenditure “urban districts” are only marginally above the national average district per-pupil expenditure of $13,553. All other urban classifications result in districts that spend less per pupil than the average school district, with three of the definitions capturing districts that spend around $1,000 less per student served. It is also worth noting the sizeable variation within each category, with standard deviations on overall spending generally well over $5,000. To put that in perspective, typical differences in spending within major urban districts can be nearly as large as the average instructional expenditures in districts that meet the Great Schools definition of urban (enrollment over 35,000).

We examine differences in within-district segregation using information theory indexes for various student groups. The index is the average deviation of each school’s racial diversity from the district-wide racial diversity, such that values of 0 indicate no segregation while values of 1 indicate complete segregation (Theil, 1972). While the proportion of Black students is relatively consistent across urban district classifications, the Black-White information index varies dramatically across and within classifications, ranging from as low as .05 and .11 in the NCES Urban and within MSA districts, to as high as .48 in the “urban intensive” districts. The general pattern holds across measures of racial, ethnic, and class isolation, confirming the well-documented conception that the large urban districts in major American cities represent some of the most segregated school systems in the country.

Although the socioeconomic characteristics of the communities surrounding urban districts vary considerably depending on the definitional rule, across the board these districts within metropolitan statistical areas have higher levels of educational attainment, single parent families, income inequality, and generally higher rates of child poverty, SNAP reliance, and renting than national averages. Median incomes within MSAs and in the rapidly growing large urban districts that we categorize as “urban emergent” are quite high at $70,000 and $65,000, respectively. They also have lower poverty rates, SNAP reliance, and slightly lower GINI coefficients and 90/10 ratios (measures of income inequality) than the other urban categorizations. All categorizations have considerably more college graduates than the typical community surrounding an American school district (~22% bachelor’s degree [BA] or higher), though the difference is dramatic in the rapidly growing “urban emergent” districts, where more than 40% of the population has at least a BA. The findings reflect the “new urbanism” broadly documented (e.g., Candipan, 2019; Pearman & Swain, 2017), where swelling cities are seeing major influxes of highly educated young (predominantly White) people, raising concerns about gentrification and displacement in high-growth, historically disinvested communities.

While it may seem counterintuitive that urban areas have both high levels of educational attainment and high levels of SNAP reliance, it can best be understood as a proximity of privilege and poverty that underlies cityscapes decorated with skyscrapers, luxury condos, dilapidated public housing, and overfilled homeless shelters. Contemporary American social and economic systems are fundamentally defined by inequality and disparities. As such, urban districts stand out as much for their assets as their disparities. Consider an urban emergent district such as Atlanta Public Schools. In 2015, 48% of adults had a bachelor’s degree or higher (the proportion of White adults with bachelor’s or more [75%] is markedly higher than Black adults with similar educational attainment [21%]), yet 37% of 5- to 17-year-olds are in poverty, 41% of adults are living in households receiving SNAP benefits (the proportion of White SNAP recipients [4%] is much lower than the proportion of Black recipients [50%]), and 59% of households with children have a single mother. The results are congruent with prior research that illustrate the existence of privilege and poverty in relatively close proximity. For instance, Reardon and colleagues (2015) ranked school districts based on the size of their racial and ethnic achievement gaps and demonstrated that the largest gaps were in small cities by major research universities like Berkley, California, and Evanston, Illinois, as well as cities that have seen major growth in their well-resourced White college educated populations in recent years, such as Atlanta, Georgia, and Washington, D.C. (Reardon et al. 2015).

Predicting urban districts

Figure 1 depicts the results of the four separate linear prediction models and shows that, all else equal, there is considerable variation in the salience of factors highlighted by the literature depending on the definition of statistical definition of an urban district. The most salient predictors of a district being urban are higher concentrations of Black and Latinx students, high levels of educational attainment of adults (percentage with a BA or higher), and the high levels of within district student segregation, suggesting that regardless how you define urban districts, they exist in communities with high levels of educational capital and higher degrees of socioeconomically and racially segregated schools. Other factors vary considerably in their importance based on the way “urban district” is defined. For example, all else equal, higher overall poverty rates make a district less likely to be designated as urban for all characterizations except the NCES Urban locale code, which includes many smaller urban communities that are more homogenously low-income than major centers of commerce. Median income is a negative predictor for all forms of “urban district,” except for “within MSA,” where higher incomes (and lower poverty rates) may positively differentiate the relatively wealthier metropolitan districts from small towns and rural districts.

Figure 1.

Coefficients from linear prediction model by urban district definition (including segregation indices).

Because the degree of racial isolation is a function of the racial composition of a district (i.e., in order to have racially isolated predominantly Black schools, a district has to have an adequate number of Black students), we also run models where we omit the measures of segregation to allow for a clearer estimate of the role of student demographic composition in driving various definitions of urbanicity. These results are presented in Figure 2 and show that the percentages of students who are Black or Latinx are, all else equal, important predictors of whether a district is urban in every definition. It is also notable that the removal of student segregation measures substantially increases the importance of economic inequality and charter school prevalence as predictors of a district’s urban status, as both are now consistently associated with increased likelihood that a district is urban (see online appendix on the journal website).

Figure 2.

Coefficients from linear prediction model by urban district definition (excluding segregation indices).

Discussion

This article seeks to extend the discourse among educational stakeholders surrounding the conceptualization and operationalization of urban education. The results indicate that no single quantitative measure fully captures the complexity of urban education—a combination of indicators is necessary but perhaps not sufficient. The results also illustrate that based on the prevailing definitions of urban education in the extant literature, a substantial number of smaller districts with similar levels of educational inequality of larger districts may be overlooked. Whether we restrict our definition to the nine largest districts in the country, or broaden the definition to include thousands of smaller districts, most definitions of “urban” identify a set of districts with high concentrations of Black and Latinx students; high levels of racial, ethnic, and socioeconomic segregation; in communities with high income inequality, high poverty, and high educational attainment. We demonstrate that other than their massive enrollment levels, the small number of large districts that is often presumed to be “urban districts,” such as New York City, Los Angeles, Houston, or Chicago, are quite similar on these measures to more inclusive measures like the Great Schools or NCES definitions that incorporate hundreds or thousands of smaller districts across the country. The rapidly growing “urban emergent” districts differ from the other urban centers in important ways, in part due to the underlying economic and shifts driving their growth. And MSAs, which currently include more White and wealthy suburbs, may provide increasingly meaningful indicators as poor students are increasingly displaced from city centers (e.g., Kneebone & Garr, 2010).

One of the challenges in defining urban school districts is the loose alignment of districts and city lines. There are certainly urban areas where the city school district is sharply divided from the suburban school systems (e.g., the Detroit schools that were the subject of Milliken v. Bradley, 1974). However, some large school districts like Wake County Public Schools in Raleigh, North Carolina (15th largest district in the country) or Jefferson County Public Schools in Louisville, Kentucky (operating budget over $1 billion) resulted from mergers between largely White suburban districts and their predominantly Black city schools. In other major metro areas, there are no clear single urban districts. Atlanta, Georgia, for example, essentially divides its more than 5 million people into at least four large school systems, each of which contains many of the characteristics the literature would describe as urban, and the largest of which, Gwinnett County, is arguably the most structurally suburban. These types of arrangements allow for greater levels of racial and class integration but also incorporate schools and neighborhoods far from city centers. As cities grow and evolve (e.g., new urbanism, gentrification), so must our definitions and focus as scholars of urban education.

(Re)Defining Urban Education

Based on our findings, we offer the following thoughts on the conceptualization and operationalization of urban education. Four tenets undergird our conceptualization. First, urban education connotes dynamic and complex rather than static and monolithic settings, with communities that continue to be shaped by the vestiges of a discriminatory and oppressive past. Too often in urban education, urban schools are framed as monolithic (Jacobs, 2015), and this bland conceptualization overlooks the growing diversity and complexity of urban educational landscape. There are vast differences within the concept of urbanness. As such, we must acknowledge the growing relativity and heterogeneity of the urban experience. As scholar Amy Stuart Wells highlighted, “in the current era, therefore, the very meaning of “urban” versus “ suburban”—once highly coded terms related to race and class—is changing rapidly” (Wells, 2014, p. 9).

Second, urban education can be defined as a continuum of conditions dependent on the characteristics, challenges, and context. Districts are arrayed along this continuum, and some districts may be more or less “urban” than others based on how they are categorized in each of these 3Cs. There are similarities in student demographics especially as more districts enroll more low-income, minority, and ELL students. Relative to rural and suburban districts, urban districts have a higher concentration of these students. Smaller districts experience some of the same challenges in terms of disparities in resources but may be distinguished by the scale and scope. Context is perhaps the area of biggest differences among districts. In urban districts, there is a history of mass immigration for economic and social reasons. Urban education is accompanied by an economy based largely on service and spanning multiple industries relative to rural and suburban districts that may be largely agrarian or based on one industry.

Third, urban education is centrally defined by the presence of educational inequality. Educational inequality exists in varying degrees across all districts. Educational inequality is the result of the interplay of outside of school factors and inside of school factors that shape educational opportunities and experiences as well as learning environments. Thus, if urban education speaks to educational inequality and we accept that these disparities in opportunities and outcomes are the results of a broad range of sociological factors, then one cannot discuss educational inequality without considering social and economic inequality. Our finding suggests that in conceptualizing urban education, it is hard to separate education from the sociocultural, sociopolitical, and socioeconomic contexts. Positioning educational inequality as central to the definition of urban education suggests that using measures of educational inequality should play a larger role in research and policy discussions about urban education.

Fourth, urban education rejects deficit perspectives and contends that considerable assets exist within “urban” communities that scholars have yet to fully discuss or empirically document. The deficit perspective that bedevils the term urban education is partly rooted in the common perception that areas with a high concentration of traditionally underserved communities are devoid of resources. In addition to highlighting the proximity of wealth and educational capital in unequal urban communities, it is imperative to further articulate the “assets of the poor” to counteract the pervasive deficit perspective that accompanies most discussions of urban education. There is much work needed in the definition and operationalization of concepts surrounding the assets that students in poverty bring to the classroom that can help educators. There is also a need to explicate the “urban appeal”—or the phenomenon that attracts gentrifiers. Future research ought to clarify the drivers of desirability of the context and cultural practices imprinted in urban settings and provide informed thought on the factors (particularly cultural and social) that are attracting young non-White college graduates to historically low-income and minority areas.

In conclusion, the definition of urban education is in a perpetual state of becoming to match an ever-changing social, economic, and educational landscape. If we use enrollment in districts as a primary criterion, we will likely overlook smaller districts, as districts like Clark County, Nevada (one of the largest school districts in the country), and Clarke County, Georgia (a district of just under 15,000 students), may have more in common in terms of educational inequality, demographic composition, and culture than many larger more homogenous systems. At the district level, urban education is defined more by inequality than by pure demographic concentration, and the changes represented by increasing gentrification amplify the evolving nature of the salience of race and class in schooling and compel us to view urban education as a liminal landscape. The challenge moving forward is therefore advancing the interpretative tradition of urban education—an iterative process of interpreting and reinterpreting the definition and conceptualization of urban education amid a confluence of significant changes in demographics as well as economic and social circumstances. When the music changes, so must the dance, and the students and the districts are changing.

Supplemental Material

WelshandSwain_ONLINEAppendix – Supplemental material for (Re)Defining Urban Education: A Conceptual Review and Empirical Exploration of the Definition of Urban Education

Supplemental material, WelshandSwain_ONLINEAppendix for (Re)Defining Urban Education: A Conceptual Review and Empirical Exploration of the Definition of Urban Education by Richard O. Welsh and Walker A. Swain in Educational Researcher

Footnotes

Authors

RICHARD O. WELSH, PhD, is an assistant professor of education leadership and policy studies in the Department of Administration, Leadership, and Technology at New York University, Pless Hall, 82 Washington Square East, 7th Floor, New York, NY 10003; row3@nyu.edu . His research focuses on the economics of education; K–12 education policy, including student discipline and school choice policies; and key mechanisms such as student mobility.

WALKER A. SWAIN, PhD, is an assistant professor in the Department of Lifelong Education, Administration and Policy in the College of Education at the University of Georgia, 850 College Station Road, 329 River’s Crossing, Athens, GA 30605; walker.swain@uga.edu . His recent work has examined intersections of education and health, teacher policy, early childhood education, neighborhood change, and school choice.

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